Mass-Loss from root bags
Adrianne Seiden
2025-08-20
Plotting mass loss over time
Loading data & defining get_stats
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
# # Path to Excel file - use relative path for portability
# file_path = "../MassLoss_processing/massLossDataProcessing.xlsx"
# Path to Excel file from local repository
import os
box_base = os.environ.get('BOX_BASE')
if box_base is None:
raise ValueError("BOX_BASE environment variable is not set!")
file_path = os.path.join(box_base, "Salk Institute Project/AKS Salk files/MassLoss_processing/massLossDataProcessing.xlsx")
# Read the 'toPlot' sheet
df = pd.read_excel(file_path, sheet_name='toPlot')
# Rename columns for clarity
df['litterbag mass remaining (%)'] = df['lb_remaining_perc._corr.']
df['sifted root mass remaining (%)'] = df['root_remaining_perc._corr.']
df['litterbag mass remaining (mg)'] = df['lb_remaining_mg_corr.']
df['sifted root mass remaining (mg)'] = df['root_remaining_mg_corr.']
df['litterbag mass remaining new (%)'] = df['lb_remaining_perc._corr.2']
print(df.head())
# Function to calculate statistics
def get_stats(data, value_col):
stats_df = data.groupby(['Isotope', 'Crop'])[value_col].agg(['mean', 'std', 'count', 'min', 'max', 'median']).reset_index()
return stats_df
# Define crop colors
crop_colors = {'noPlant': '#9F29FF', 'wheat': '#98C65D', 'rice': '#FC9D33', 'soy': '#FE318E'}> Sample_number ... litterbag mass remaining new (%)
> 0 1 ... 100.0
> 1 2 ... 100.0
> 2 3 ... 100.0
> 3 4 ... 100.0
> 4 5 ... 100.0
>
> [5 rows x 15 columns]Litterbag mass remaining over time - percent
# Plot 1: remaining/noPlant_percent vs Week, grouped by Isotope and Crop
plt.figure(figsize=(12, 8))
sns.lineplot(data=df, x='Time (weeks)', y='litterbag mass remaining (%)', hue='Crop', style='Isotope', markers=True, palette=crop_colors)
plt.title('Litterbag Mass Remaining (%) vs Time (weeks) (Grouped by Isotope and Crop)')
plt.xlabel('Time (weeks)')
plt.ylabel('Litterbag Mass Remaining (%)')
plt.legend(title='Isotope / Crop')
plt.show()
# Statistics for Plot 1
stats_df1 = get_stats(df, 'litterbag mass remaining (%)')
print("Statistics for Plot 1:\n", stats_df1)
# Plot 2: remaining/noPlant_percent vs Time (weeks), grouped by Crop only
plt.figure(figsize=(12, 8))
sns.lineplot(data=df, x='Time (weeks)', y='litterbag mass remaining (%)', hue='Crop', marker='o', palette=crop_colors)
plt.title('Litterbag Mass Remaining (%) vs Time (weeks) (Grouped by Crop Only)')
plt.xlabel('Time (weeks)')
plt.ylabel('Litterbag Mass Remaining (%)')
plt.legend(title='Crop')
plt.show()
# Statistics for Plot 2
stats_df2 = get_stats(df, 'litterbag mass remaining (%)')
print("Statistics for Plot 2:\n", stats_df2)
> Statistics for Plot 1:
> Isotope Crop mean ... min max median
> 0 12 noPlant 102.074571 ... 94.928072 137.760416 100.0
> 1 12 rice 110.644934 ... 92.338602 189.561282 100.0
> 2 12 soy 103.722970 ... 80.566872 166.900554 100.0
> 3 13 rice 109.016668 ... 86.892651 174.279573 100.0
> 4 13 soy 107.833936 ... 81.260888 252.242054 100.0
> 5 13 wheat 104.122333 ... 87.229973 199.754897 100.0
>
> [6 rows x 8 columns]
> Statistics for Plot 2:
> Isotope Crop mean ... min max median
> 0 12 noPlant 102.074571 ... 94.928072 137.760416 100.0
> 1 12 rice 110.644934 ... 92.338602 189.561282 100.0
> 2 12 soy 103.722970 ... 80.566872 166.900554 100.0
> 3 13 rice 109.016668 ... 86.892651 174.279573 100.0
> 4 13 soy 107.833936 ... 81.260888 252.242054 100.0
> 5 13 wheat 104.122333 ... 87.229973 199.754897 100.0
>
> [6 rows x 8 columns]Trying another correction
Dividing the percent mass remaining by the average T0 percent remaining (after 2 days), grouped by Isotope and Crop. Previous chunk divided by average noPlant mass remaining (across all time points).
# Plot 1: remaining/noPlant_percent vs Week, grouped by Isotope and Crop
plt.figure(figsize=(12, 8))
sns.lineplot(data=df, x='Time (weeks)', y='litterbag mass remaining new (%)', hue='Crop', style='Isotope', markers=True, palette=crop_colors)
plt.title('Litterbag Mass Remaining (%) vs Time (weeks) (Grouped by Isotope and Crop)')
plt.xlabel('Time (weeks)')
plt.ylabel('Litterbag Mass Remaining (%)')
plt.legend(title='Isotope / Crop')
plt.show()
# Statistics for Plot 1
stats_dfn = get_stats(df, 'litterbag mass remaining new (%)')
print("Statistics for Plot 1:\n", stats_dfn)
# Plot 2: remaining/noPlant_percent vs Time (weeks), grouped by Crop only
plt.figure(figsize=(12, 8))
sns.lineplot(data=df, x='Time (weeks)', y='litterbag mass remaining new (%)', hue='Crop', marker='o', palette=crop_colors)
plt.title('Litterbag Mass Remaining (%) vs Time (weeks) (Grouped by Crop Only)')
plt.xlabel('Time (weeks)')
plt.ylabel('Litterbag Mass Remaining (%)')
plt.legend(title='Crop')
plt.show()
# Statistics for Plot 2
stats_dfn1 = get_stats(df, 'litterbag mass remaining new (%)')
print("Statistics for Plot 2:\n", stats_dfn1)
> Statistics for Plot 1:
> Isotope Crop mean ... min max median
> 0 12 noPlant 101.133742 ... 93.213011 135.271505 100.0
> 1 12 rice 106.531191 ... 86.074974 176.702723 100.0
> 2 12 soy 101.611298 ... 77.400055 160.340246 100.0
> 3 13 rice 90.602930 ... 59.781352 119.902757 100.0
> 4 13 soy 81.952333 ... 44.895353 139.359738 100.0
> 5 13 wheat 87.889272 ... 61.066846 139.841858 100.0
>
> [6 rows x 8 columns]
> Statistics for Plot 2:
> Isotope Crop mean ... min max median
> 0 12 noPlant 101.133742 ... 93.213011 135.271505 100.0
> 1 12 rice 106.531191 ... 86.074974 176.702723 100.0
> 2 12 soy 101.611298 ... 77.400055 160.340246 100.0
> 3 13 rice 90.602930 ... 59.781352 119.902757 100.0
> 4 13 soy 81.952333 ... 44.895353 139.359738 100.0
> 5 13 wheat 87.889272 ... 61.066846 139.841858 100.0
>
> [6 rows x 8 columns]Root mass remaining over time - percent
# Plot 3: remaining_root/cleanBag_percent vs Time (weeks), grouped by Isotope and Crop
plt.figure(figsize=(12, 8))
sns.lineplot(data=df, x='Time (weeks)', y='sifted root mass remaining (%)', hue='Crop', style='Isotope', markers=True, palette=crop_colors)
plt.title('Sifted Root Mass Remaining (%) vs Time (weeks) (Grouped by Isotope and Crop)')
plt.xlabel('Time (weeks)')
plt.ylabel('Sifted Root Mass Remaining (%)')
plt.legend(title='Isotope / Crop')
plt.show()
# Statistics for Plot 3
stats_df3 = get_stats(df, 'sifted root mass remaining (%)')
print("Statistics for Plot 3:\n", stats_df3)
# Plot 4: remaining_root/cleanBag_percent vs Time (weeks), grouped by Crop only
plt.figure(figsize=(12, 8))
sns.lineplot(data=df, x='Time (weeks)', y='sifted root mass remaining (%)', hue='Crop', marker='o', palette=crop_colors)
plt.title('Sifted Root Mass Remaining (%) vs Time (weeks) (Grouped by Crop Only)')
plt.xlabel('Time (weeks)')
plt.ylabel('Sifted Root Mass Remaining (%)')
plt.legend(title='Crop')
plt.show()
# Statistics for Plot 4
stats_df4 = get_stats(df, 'sifted root mass remaining (%)')
print("Statistics for Plot 4:\n", stats_df4)
> Statistics for Plot 3:
> Isotope Crop mean ... min max median
> 0 12 noPlant 100.000000 ... 100.000000 100.000000 100.0
> 1 12 rice 100.840695 ... 92.035454 125.097049 100.0
> 2 12 soy 99.786563 ... 92.665753 103.155015 100.0
> 3 13 rice 100.000000 ... 100.000000 100.000000 100.0
> 4 13 soy 100.000000 ... 100.000000 100.000000 100.0
> 5 13 wheat 100.000000 ... 100.000000 100.000000 100.0
>
> [6 rows x 8 columns]
> Statistics for Plot 4:
> Isotope Crop mean ... min max median
> 0 12 noPlant 100.000000 ... 100.000000 100.000000 100.0
> 1 12 rice 100.840695 ... 92.035454 125.097049 100.0
> 2 12 soy 99.786563 ... 92.665753 103.155015 100.0
> 3 13 rice 100.000000 ... 100.000000 100.000000 100.0
> 4 13 soy 100.000000 ... 100.000000 100.000000 100.0
> 5 13 wheat 100.000000 ... 100.000000 100.000000 100.0
>
> [6 rows x 8 columns]Litterbag mass remaining over time - milligrams
# Plot 5: remaining root mass (mg) vs Time (weeks), grouped by Isotope and Crop
plt.figure(figsize=(12, 8))
sns.lineplot(data=df, x='Time (weeks)', y='litterbag mass remaining (mg)', hue='Crop', style='Isotope', markers=True, palette=crop_colors)
plt.title('Litterbag Mass Remaining (mg) vs Time (weeks) (Grouped by Isotope and Crop)')
plt.xlabel('Time (weeks)')
plt.ylabel('Litterbag Mass Remaining (mg)')
plt.legend(title='Isotope / Crop')
plt.show()
# Statistics for Plot 5
stats_df5 = get_stats(df, 'litterbag mass remaining (mg)')
print("Statistics for Plot 5:\n", stats_df5)
# Plot 6: remaining root mass (mg) vs Time (weeks), grouped by Crop only
plt.figure(figsize=(12, 8))
sns.lineplot(data=df, x='Time (weeks)', y='litterbag mass remaining (mg)', hue='Crop', marker='o', palette=crop_colors)
plt.title('Litterbag Mass Remaining (mg) vs Time (weeks) (Grouped by Crop Only)')
plt.xlabel('Time (weeks)')
plt.ylabel('Litterbag Mass Remaining (mg)')
plt.legend(title='Crop')
plt.show()
# Statistics for Plot 6
stats_df6 = get_stats(df, 'litterbag mass remaining (mg)')
print("Statistics for Plot 6:\n", stats_df6)
> Statistics for Plot 5:
> Isotope Crop mean ... min max median
> 0 12 noPlant 71.685531 ... 66.931674 97.534375 70.150000
> 1 12 rice 159.552628 ... 133.863348 254.959924 144.900000
> 2 12 soy 146.960662 ... 114.243825 237.499488 142.050000
> 3 13 rice 155.774256 ... 122.692423 247.825552 143.050000
> 4 13 soy 145.050531 ... 116.215164 313.536873 133.250054
> 5 13 wheat 101.186028 ... 85.049224 179.579652 96.650000
>
> [6 rows x 8 columns]
> Statistics for Plot 6:
> Isotope Crop mean ... min max median
> 0 12 noPlant 71.685531 ... 66.931674 97.534375 70.150000
> 1 12 rice 159.552628 ... 133.863348 254.959924 144.900000
> 2 12 soy 146.960662 ... 114.243825 237.499488 142.050000
> 3 13 rice 155.774256 ... 122.692423 247.825552 143.050000
> 4 13 soy 145.050531 ... 116.215164 313.536873 133.250054
> 5 13 wheat 101.186028 ... 85.049224 179.579652 96.650000
>
> [6 rows x 8 columns]Root mass remaining over time - milligrams
# Plot 7: remaining root mass (mg) vs Time (weeks), grouped by Isotope and Crop
plt.figure(figsize=(12, 8))
sns.lineplot(data=df, x='Time (weeks)', y='sifted root mass remaining (mg)', hue='Crop', style='Isotope', markers=True, palette=crop_colors)
plt.title('Sifted Root Mass Remaining (mg) vs Time (weeks) (Grouped by Isotope and Crop)')
plt.xlabel('Time (weeks)')
plt.ylabel('Sifted Root Mass Remaining (mg)')
plt.legend(title='Isotope / Crop')
plt.show()
# Statistics for Plot 7
stats_df7 = get_stats(df, 'sifted root mass remaining (mg)')
print("Statistics for Plot 7:\n", stats_df7)
# Plot 8: remaining root mass (mg) vs Time (weeks), grouped by Crop only
plt.figure(figsize=(12, 8))
sns.lineplot(data=df, x='Time (weeks)', y='sifted root mass remaining (mg)', hue='Crop', marker='o', palette=crop_colors)
plt.title('Sifted Root Mass Remaining (mg) vs Time (weeks) (Grouped by Crop Only)')
plt.xlabel('Time (weeks)')
plt.ylabel('Sifted Root Mass Remaining (mg)')
plt.legend(title='Crop')
plt.show()
# Statistics for Plot 8
stats_df8 = get_stats(df, 'sifted root mass remaining (mg)')
print("Statistics for Plot 8:\n", stats_df8)
> Statistics for Plot 7:
> Isotope Crop mean std count min max median
> 0 12 noPlant 0.313593 0.863657 20 0.0 3.462379 0.00
> 1 12 rice 74.966245 7.570415 20 63.8 103.079968 73.85
> 2 12 soy 71.199356 4.227155 20 63.4 83.300000 71.35
> 3 13 rice 72.187500 2.496364 16 68.3 77.600000 72.10
> 4 13 soy 64.856250 10.100097 16 53.5 92.300000 62.25
> 5 13 wheat 26.862500 5.868546 16 19.1 39.100000 25.95
> Statistics for Plot 8:
> Isotope Crop mean std count min max median
> 0 12 noPlant 0.313593 0.863657 20 0.0 3.462379 0.00
> 1 12 rice 74.966245 7.570415 20 63.8 103.079968 73.85
> 2 12 soy 71.199356 4.227155 20 63.4 83.300000 71.35
> 3 13 rice 72.187500 2.496364 16 68.3 77.600000 72.10
> 4 13 soy 64.856250 10.100097 16 53.5 92.300000 62.25
> 5 13 wheat 26.862500 5.868546 16 19.1 39.100000 25.95